为患有喘的老年人开发基于机器学习的抑郁风险识别工具
Lin An1, Xi Wang2, Liuqun Jia2
1Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China. anlin0805@163.com.
BMC psychiatry
|September 24, 2025
概括
一个新的机器学习工具,抑郁风险识别工具 (DRIT),可以预测患有喘的老年人抑郁风险. 这有助于早期干预和综合护理,以改善患者的治疗结果.
科学领域:
- 老年学是一门学科.
- 肺部病理学 肺部病理学
- 精神病学是一个精神病学.
背景情况:
- 喘显著影响老年人的生活质量.
- 伴随性抑郁症使喘管理复杂化,并使健康状况恶化.
- 对这一群体来说,早期识别抑郁风险至关重要.
研究的目的:
- 开发一种基于机器学习的工具,用于预测患有喘的老年人抑郁风险.
- 在这个人口群体中确定抑郁症的关键预测因素.
- 促进及时干预和综合护理.
主要方法:
- 中国健康与退休长度研究 (CHARLS) 数据的二次分析.
- 利用LASSO回归来确定21个显著的抑郁症预测因素.
- 评估了八个机器学习算法,根据AUC和准确性选择了glmBoost模型.
主要成果:
- glmBoost模型在测试队列中达到0.740的AUC,在验证队列中达到0.664.
- 确定了关键的风险因素:认知功能差,大量运动,未婚状态和女性性别.
- SHAP值增强了模型的解释性,详细说明了预测器的贡献.
结论:
- 开发的抑郁风险识别工具 (DRIT) 有效预测老年喘患者的抑郁风险.
- 能够及时识别和干预,有可能改善患者的治疗结果.
- 促进喘和抑郁症的综合管理,减少医疗保健负担.
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